Sage Journals: Discover world-class research

Abstract

Design of experiments has been and continues to be employed successfully for the robust design of products/processes. A principle contention in these areas of application concerns whether or not interactive effects should be estimated. The arguments, for and against, hinge on an experimenter's understanding of the interaction concept and on the value added that is derived from the increased effort required for assessing such effects. We propose an interactive graphics approach that assesses whether and which interactive effects to estimate. Our methodology yields several additional benefits to the experimenter that include prototype simulation and unique learning opportunities for augmenting process knowledge in post‐analysis.

Keywords

PRODUCT/PROCESS DESIGN DESIGN OF EXPERIMENTS INTERACTION EFFECTS ELICITATION TECHNIQUES VISUALIZATION.

Get full access to this article

View all access options for this article.

References

Anderson

N. H.

. (1981), Foundations of Information Integration Theory, Academic Press, New York.

Box

and Bisgaard

(1987), “The Scientific Context of Quality Improvement,” Quality Progress, 54–61.

Box

and Draper

(1987), Empirical Model‐Building and Response Surfaces. John Wiley & Sons, Inc., New York.

Box

and Draper

(1969), Evolutionary Operation, A Statistical Model for Process Improvement, John Wiley & Sons, Inc., New York.

Box

Hunter

W. G.

, and Hunter

J. S.

(1978), Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building, John Wiley & Sons, Inc., New York.

Coleman

and Montgomery

(1993), “A Systematic Approach to Planning for a Designed Industrial Experiment,” Technometrics, 35, 1, 1–12.

Enrick

. (1983), Manufacturing Analysis for Productivity and Quality/Cost Enhancement, Industrial Press, Inc., New York.

Franklin

. (1985) “Selecting Defining Contrasts and Confounded Effects in p(n‐m) Factorial Experiments,” Technometrics, 27, 165–172.

Gunter

. (1993), “Discussion,” Technometrics, 35, 1, 13–14.

10.

Haaland

, and O'Connell

M. A.

(1993), “Discussion,” Technometrics, 35, 1, 17–20.

11.

Hahn

(1993), “Discussion,” Technometrics, 35, 1, 15–17.

12.

Hunter

. (1985), “Statistical Design Applied to Product Design,” Journal of Quality Technology, 17, 4, 210–221.

13.

Johnson

. (1974) “Trade—Off Analysis of Consumer Values,” Journal of Marketing Research, 11, 121–127.

14.

Kackar

. (1985), “Off‐Line Quality Control Parameter Design, and the Taguchi Method,” Journal of Quality Technology, 17, 4, 176–188.

15.

Kackar

and Tsui

(1990) “Interaction Graphs: Graphical Aids for Planning Experiments,” Journal of Quality Technology, 22, 1–14.

16.

Kruskal

. (1965), “Analysis of Factorial Experiments by Estimating Monotone Transformations of the Data,” Journal of the Royal Statistical Society, 27, 1, 251–263.

17.

Leon

Shoemaker

, and Tsui

(1993), “Discussion,” Technometrics, 35, 1, 21–24.

18.

Moskowitz

and Kim

(1995), QFD Optimizer: A Novice Friendly Decision Support System for Optimizing Product/Service Designs, Working Paper, Center for the Management of Manufacturing Enterprises, Purdue University, Purdue, IN.

19.

Radson

, and Herrin

(1995), “Augmenting a Factorial Experiment When One Factor is an Uncontrollable Random Variable: A Case Study,” Technometrics, 37, 1.

20.

Srinivasan

, and Shocker

(1973), “Estimating the Weights for Multiple Attributes in a Composite Criterion Using Pairwise Judgments,” Psychometrika, 38, 4, 473–493.

21.

Sun

, and Wu

(1994), “Interaction Graphs for Three‐Level Fractional Factorial Designs,” Journal of Quality Technology, 26, 4.

22.

Taguchi

. (1987), System of Experimental Design. UNIPUB, New York, NY.

23.

, and Chen

(1992) “A Graph‐Aided Method for Planning Two‐Level Experiments When Certain Interactions are Important,” Technometrics, 34, 162–175.